Jifeng Shen

A novel distribution-based feature for rapid object detection

Neurocomputing, 2011

The discriminative power of a feature has an impact on the convergence rate in training and runni... more The discriminative power of a feature has an impact on the convergence rate in training and running speed in evaluating an object detector. In this paper, a novel distribution-based discriminative feature is proposed to distinguish objects of rigid object categories from background. It fully makes use of the advantage of local binary pattern (LBP) that specializes in encoding local structures and statistic information of distribution from training data, which is utilized in getting optimal separating hyperplane. The proposed feature maintains the merit of simplicity in calculation and powerful discriminative ability to distinguish objects from background patches. Three LBP-based features are derived to adaptive projection ones, which are more discriminative than original versions. The asymmetric Gentle Adaboost organized in nested cascade structure constructs the final detector. The proposed features are evaluated on two different object categories: frontal human faces and side-view cars. Experimental results demonstrate that the proposed features are more discriminative than traditional Haarlike features and multi-block LBP (MBLBP) features. Furthermore they are also robust in monotonous variations of illumination.

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Face Recognition Using DT-CWT Feature-Based 2DPCA

2010 Chinese Conference on Pattern Recognition (CCPR), 2010

In face recognition, a way to enhance the discriminability is to provide effective feature repres... more In face recognition, a way to enhance the discriminability is to provide effective feature representation. Dual-Tree Complex Wavelet transform (DT-CWT) provides a local multiscale description of images with good directional selectivity and shift invariance, and is robust to illumination variations and facial expression changes. In this paper, we propose a novel approach to face feature extraction and recognition using DT-CWT

Feature extraction using 2DIFDA with fuzzy membership

Soft Computing, 2012

ABSTRACT In this paper, a new method called fuzzy two-dimensional inverse Fisher discriminant ana... more ABSTRACT In this paper, a new method called fuzzy two-dimensional inverse Fisher discriminant analysis (fuzzy 2DIFDA) directly based on 2D image matrices rather than image vectors is proposed for feature extraction and recognition. In the proposed method, the distribution information of samples is first characterized using fuzzy set theory, and the corresponding fuzzy scatter matrices are then redefined. Image discriminant features which have embedded the fuzzy information are finally extracted by selecting 2D principal components and 2D inverse Fisher discriminant vectors. Experimental results on FERET face database and FKP database demonstrate the effectiveness of the proposed method.

A novel distribution-based feature for rapid object detection

Neurocomputing, 2011

The discriminative power of a feature has an impact on the convergence rate in training and runni... more The discriminative power of a feature has an impact on the convergence rate in training and running speed in evaluating an object detector. In this paper, a novel distribution-based discriminative feature is proposed to distinguish objects of rigid object categories from background. It fully makes use of the advantage of local binary pattern (LBP) that specializes in encoding local structures and statistic information of distribution from training data, which is utilized in getting optimal separating hyperplane. The proposed feature maintains the merit of simplicity in calculation and powerful discriminative ability to distinguish objects from background patches. Three LBP-based features are derived to adaptive projection ones, which are more discriminative than original versions. The asymmetric Gentle Adaboost organized in nested cascade structure constructs the final detector. The proposed features are evaluated on two different object categories: frontal human faces and side-view cars. Experimental results demonstrate that the proposed features are more discriminative than traditional Haarlike features and multi-block LBP (MBLBP) features. Furthermore they are also robust in monotonous variations of illumination.

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Face Recognition Using DT-CWT Feature-Based 2DPCA

2010 Chinese Conference on Pattern Recognition (CCPR), 2010

In face recognition, a way to enhance the discriminability is to provide effective feature repres... more In face recognition, a way to enhance the discriminability is to provide effective feature representation. Dual-Tree Complex Wavelet transform (DT-CWT) provides a local multiscale description of images with good directional selectivity and shift invariance, and is robust to illumination variations and facial expression changes. In this paper, we propose a novel approach to face feature extraction and recognition using DT-CWT

Feature extraction using 2DIFDA with fuzzy membership

Soft Computing, 2012

ABSTRACT In this paper, a new method called fuzzy two-dimensional inverse Fisher discriminant ana... more ABSTRACT In this paper, a new method called fuzzy two-dimensional inverse Fisher discriminant analysis (fuzzy 2DIFDA) directly based on 2D image matrices rather than image vectors is proposed for feature extraction and recognition. In the proposed method, the distribution information of samples is first characterized using fuzzy set theory, and the corresponding fuzzy scatter matrices are then redefined. Image discriminant features which have embedded the fuzzy information are finally extracted by selecting 2D principal components and 2D inverse Fisher discriminant vectors. Experimental results on FERET face database and FKP database demonstrate the effectiveness of the proposed method.

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